Medical image scanning data, for example, is typically obtained in the form of slices in various types of imaging modalities. These slices are then stacked to form a three-dimensional (3D) volume.
In the case of cardiovascular applications, for example, image registration and segmentation is often difficult in magnetic resonance (MR) imaging due to a lack of contrast between features, and/or due to artifacts in the images. This problem is compounded when fast imaging methods are used at the price of the signal-to-noise ratio (SNR).
Existing approaches to image registration and segmentation work with varying degrees of success, and some are dependent on a priori knowledge of the structure under investigation. Accordingly, it is desirable in many cardiovascular applications to have an automatic, accurate and robust technique for image registration and segmentation, particularly where an organ is in motion.